6 Master Data is Non-transactional data A noun/entity used within the business Used to describe any transaction, group of transactions, or other data entity Slowly changing, relative to the overall rate of transactions Any relevant reference data Any associated attributes, properties, or relationships that define, classify, describe, or enhance master data Master Data Management therefore contains a substantial amount of entity and attribute modeling combined with respective metadata.

8 Master Data Management Measurement recognizes the importance of metrics to sustain focus and progress. This includes data quality measurement and individual performance management considerations. Technology specifies the target MDM architectural style evolution and identifies how existing technologies may be used for the solution. It also elaborates the role of a serviceoriented architecture. Governance Directives that manage the organizational bodies, policies, principles, and qualities to promote access to accurate and certified master data Organization depicts the roles, responsibilities, and relationships of those participating in the program. Standards establish the guidelines and rules for creating and maintaining master data. This includes rule-sets for data architecture, data modeling, and the creation of data domains and elements. Process specifies new processes to be introduced and identifies considerations for modifications to existing master data processes which will be required to achieve the target state.

9 The challenges of data modeling in a Master Data Management initiative Copyright

11 Key MD Modeling Challenge: Adaptive & collaborative modeling through the entire lifecycle It's important to note that traditional approaches to Master Data Management (MDM) will often motivate the creation and maintenance of detailed LDMs, an effort that is rarely justifiable in practice when you consider the total cost of ownership (TCO) when calculating the return on investment (ROI) of those sorts of efforts. Extracts from 12 Critical Lessons in Agile Data Modeling 1. Agile modelers create agile models which are just barely good enough. 2. Agile developers solve today s problem today and trust they can solve tomorrow s problem tomorrow. 3. Agile data modeling is both evolutionary and collaborative. 5. Agile data models can and should follow your corporate standards. 6. Trying to define all the requirements upfront is a risky proposition.. Interesting data modeling challenges. at literally every step in the MDM application lifecycle. Acknowledgement: Scott W. Ambler

17 Points to Consider on Industry Models Balance data, process and use case requirements. Don t let a data model or pattern preference dictate an entire physical application design. Check sources of reference data. (Entity by entity if necessary) Validate any professional memberships or subscriptions required for ongoing access. Obtain the source for any integration to obtain data & populate the tables. At some point you will need to customize this code for a future requirement. Ensure there is an easy way to hand pick & utilize patterns, groups of entities etc. Be careful of defaulting a physical model to an industry model. Generally, an industry model should assist design process not dictate a physical design. Opt for productized application layer rather than instance-specific application coding. Deliver higher return on investment and shorter time-to-value. Easier to maintain an application in the longer term if less pieces break with change.

18 Building your own MDM Center of Excellence Copyright

19 The Cornerstone of Information Management Single Version of the Truth Almost every piece of valuable information in an organization is identified, calculated, stored, retrieved, analyzed, reported and utilized based on its categorization by master data. Without a single version of master data, creating a single version of information is virtually impossible. Accuracy Every system in an organization from an ERP to a spreadsheet uses master data in order to store and display information in a meaningful way. Inaccurate or inconsistent master data, or its use across systems, is the largest driver of errors in reports and analytical information. Timeliness Integration Consistency Consolidation Quality Productivity Mapping

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